Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting. (arXiv:2211.15856v2 [cs.LG] UPDATED)

Producing high-quality forecasts of key climate variables such as temperature
and precipitation on subseasonal time scales has long been a gap in operational
forecasting. Recent studies have shown promising results using machine learning
(ML) models to advance subseasonal forecasting (SSF), but several open
questions remain. First, several past approaches use the average of an ensemble
of physics-based forecasts as an input feature of these models. However,
ensemble forecasts contain information that can aid prediction beyond only the
ensemble mean. Second, …

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